Pattern Generation and Information Transfer through a Liquid/Liquid Interface in 3D Constitutional Dynamic Networks of Imine Ligands in Response to Metal Cation Effectors

2019 ◽  
Vol 141 (32) ◽  
pp. 12724-12737 ◽  
Author(s):  
Artem Osypenko ◽  
Sébastien Dhers ◽  
Jean-Marie Lehn
Keyword(s):  
Metal Cation ◽  
Information Transfer ◽  
Dynamic Networks ◽  
Liquid Interface ◽  
Pattern Generation ◽  
Imine Ligands

scholarly journals Integration of photocatalytic and dark-operating catalytic biomimetic transformations through DNA-based constitutional dynamic networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chen Wang ◽  
Michael P. O’Hagan ◽  
Ehud Neumann ◽  
Rachel Nechushtai ◽  
Itamar Willner
Keyword(s):  
Nucleic Acid ◽  
Energy Harvesting ◽  
Information Transfer ◽  
Dynamic Networks ◽  
Research Direction ◽  
Cellular Biology ◽  
Dynamic Feedback ◽  
Dynamic Coupling ◽  
Metabolic Processes ◽  
Catalytic Functions

AbstractNucleic acid-based constitutional dynamic networks (CDNs) have recently emerged as versatile tools to control a variety of catalytic processes. A key challenge in the application of these systems is achieving intercommunication between different CDNs to mimic the complex interlinked networks found in cellular biology. In particular, the possibility to interface photochemical ‘energy-harvesting’ processes with dark-operating ‘metabolic’ processes, in a similar way to plants, represents an up to now unexplored yet enticing research direction. The present study introduces two CDNs that allow the intercommunication of photocatalytic and dark-operating catalytic functions mediated by environmental components that facilitate the dynamic coupling of the networks. The dynamic feedback-driven intercommunication of the networks is accomplished via information transfer between the two CDNs effected by hairpin fuel strands in the environment of the system, leading to the coupling of the photochemical and dark-operating modules.

scholarly journals Ebola Impact and Quarantine in a Network Model

2016 ◽  
Vol 13 (4) ◽  
Author(s):  
Anca Radulescu ◽  
Joanna Herron
Keyword(s):  
Information Transfer ◽  
Short Range ◽  
Compartmental Model ◽  
Dynamic Networks ◽  
Network Connectivity ◽  
Disease Dynamics ◽  
Epidemic Spread ◽  
Connectivity Graph ◽  
Connectivity Patterns ◽  
Local Parameters

Much effort has been directed towards using mathematical models to understand and predict contagious disease, in particular Ebola outbreaks. Classical SIR (susceptible-infected-recovered) compartmental models capture well the dynamics of the outbreak in certain communities, and accurately describe the differences between them based on a variety of parameters. However, repeated resurgence of Ebola contagions suggests that there are components of the global disease dynamics that we don’t yet fully understand and can’t effectively control. In order to understand the dynamics of a more widespread contagion, we placed SIR models within the framework of dynamic networks, with the communities at risk of contracting the virus acting as nonlinear systems, coupled based on a connectivity graph. We study how the effects of the disease (measured as the outbreak impact and duration) change with respect to local parameters, but also with changes in both short-range and long-range connectivity patterns in the graph. We discuss the implications of optimizing both these measures in increasingly realistic models of coupled communities. KEYWORDS: Epidemic Spread; Network Dynamics; Network Connectivity; Coupled Differential Equations; Compartmental Model; Information Transfer; Outbreak Impact; Outbreak Duration

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Ultimate resolution and information in electron microscopy

1991 ◽  
Vol 49 ◽  
pp. 496-497
Author(s):  
D. Van Dyck
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Transfer Function ◽  
Information Transfer ◽  
Communication Channel ◽  
Structural Information ◽  
Information Rate ◽  
Noise Power ◽  
Signal Power ◽  
Imaging Device

An (electron) microscope can be considered as a communication channel that transfers structural information between an object and an observer. In electron microscopy this information is carried by electrons. According to the theory of Shannon the maximal information rate (or capacity) of a communication channel is given by C = B log2 (1 + S/N) bits/sec., where B is the band width, and S and N the average signal power, respectively noise power at the output. We will now apply to study the information transfer in an electron microscope. For simplicity we will assume the object and the image to be onedimensional (the results can straightforwardly be generalized). An imaging device can be characterized by its transfer function, which describes the magnitude with which a spatial frequency g is transferred through the device, n is the noise. Usually, the resolution of the instrument ᑭ is defined from the cut-off 1/ᑭ beyond which no spadal information is transferred.

What Is Left Is Right

2009 ◽  
Vol 14 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Kenneth Hugdahl ◽  
René Westerhausen
Keyword(s):  
Speech Processing ◽  
Information Transfer ◽  
Hemispheric Asymmetry ◽  
Functional Neuroimaging ◽  
Gray Matter Volume ◽  
Functional Anatomy ◽  
Posterior Part ◽  
Functional Asymmetry ◽  
Planum Temporale ◽  
Evolution Of Speech

The present paper is based on a talk on hemispheric asymmetry given by Kenneth Hugdahl at the Xth European Congress of Psychology, Praha July 2007. Here, we propose that hemispheric asymmetry evolved because of a left hemisphere speech processing specialization. The evolution of speech and the need for air-based communication necessitated division of labor between the hemispheres in order to avoid having duplicate copies in both hemispheres that would increase processing redundancy. It is argued that the neuronal basis of this labor division is the structural asymmetry observed in the peri-Sylvian region in the posterior part of the temporal lobe, with a left larger than right planum temporale area. This is the only example where a structural, or anatomical, asymmetry matches a corresponding functional asymmetry. The increase in gray matter volume in the left planum temporale area corresponds to a functional asymmetry of speech processing, as indexed from both behavioral, dichotic listening, and functional neuroimaging studies. The functional anatomy of the corpus callosum also supports such a view, with regional specificity of information transfer between the hemispheres.

A distributed cognition approach to understanding information transfer in mission critical domains

2006 ◽  
Author(s):  
Ayse P. Gurses ◽  
Yan Xiao ◽  
Paul Gorman ◽  
Brian Hazlehurst ◽  
Grant Bochicchio ◽  
...  
Keyword(s):  
Distributed Cognition ◽  
Information Transfer ◽  
Mission Critical
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